Unravelling the Diversity of Microorganisms in Ticks from Australian Wildlife

Abstract

Ticks and tick-borne pathogens pose a significant threat to the health and welfare of humans and animals. Our knowledge about pathogens carried by ticks of Australian wildlife is limited. This study aimed to characterise ticks and tick-borne microorganisms from a range of wildlife species across six sites in Victoria, Australia. Following morphological and molecular characterisation (targeting 16S rRNA and cytochrome c oxidase I), tick DNA extracts (n = 140) were subjected to microfluidic real-time PCR-based screening for the detection of microorganisms and Rickettsia-specific real-time qPCRs. Five species of ixodid ticks were identified, including Aponomma auruginans, Ixodes (I.) antechini, I. kohlsi, I. tasmani and I. trichosuri. Phylogenetic analyses of 16S rRNA sequences of I. tasmani revealed two subclades, indicating a potential cryptic species. The microfluidic real-time PCR detected seven different microorganisms as a single (in 13/45 ticks) or multiple infections (27/45). The most common microorganisms detected were Apicomplexa (84.4%, 38/45) followed by Rickettsia sp. (55.6%, 25/45), Theileria sp. (22.2% 10/45), Bartonella sp. (17.8%, 8/45), Coxiella-like sp. (6.7%, 3/45), Hepatozoon sp. (2.2%, 1/45), and Ehrlichia sp. (2.2%, 1/45). Phylogenetic analyses of four Rickettsia loci showed that the Rickettsia isolates detected herein potentially belonged to a novel species of Rickettsia. This study demonstrated that ticks of Australian wildlife carry a diverse array of microorganisms. Given the direct and indirect human-wildlife-livestock interactions, there is a need to adopt a One Health approach for continuous surveillance of tick-associated pathogens/microorganisms to minimise the associated threats to animal and human health.

Keywords: Australia; Rickettsia; tick-borne pathogens; ticks; wildlife; zoonosis.

Figure 1

Map showing the total number of ticks collected from six different hosts from urban and regional areas of Victoria, Australia. (A), map of Australia; (B), map of Victoria.

Figure 2

Genetic relationships of 16S rRNA gene (A) and cytochrome c oxidase subunit I gene (B) sequences of ticks collected from Australian wild animals from Victoria. The 16S rRNA (370 bp) and cox1 (612 bp) datasets were analysed using Neighbor Joining (NJ), Maximum Likelihood (ML) and Bayesian Inference (BI) methods. There was a concordance among the topology of the BI, ML, and NJ trees (not shown); only the ML tree is presented here. Nodal support is given as a posterior probability of BI and bootstrap values for NJ and ML. Sequences obtained in this study are shown in bold fonts. The trees were rooted using Ornithodoros moubata as an outgroup. Each scale bar indicates the number of inferred substitutions per site.

Figure 3

The number of co-occurring microorganisms (along x-axis) found in tested ticks (number provided along y-axis) of each species.

Figure 4

Genetic relationships of the citrate synthase (gltA) gene (A), outer membrane protein A (ompA) gene (B), 17 kDa gene (C), and outer membrane protein B (ompB) gene (D) sequences of Rickettsia sp. detected in ticks collected from Australian wild animals from Victoria. The sequence data (in bp) (352 (gltA), 434 (ompA), 391 (17 kDa), and 267 (ompB)) for each locus were separately analysed using Neighbor Joining (NJ), Maximum Likelihood (ML), and Bayesian Inference (BI) methods. There was a concordance among the topologies of the BI, ML, and NJ trees (not shown); only ML trees are presented here. Nodal support is given as a posterior probability of BI and bootstrap values for NJ and ML. Sequences obtained in this study are shown in bold fonts. Trees were rooted using R. felis and R. bellii as outgroups. Each scale bar indicates the number of inferred substitutions per site.

Figure 4

Genetic relationships of the citrate synthase (gltA) gene (A), outer membrane protein A (ompA) gene (B), 17 kDa gene (C), and outer membrane protein B (ompB) gene (D) sequences of Rickettsia sp. detected in ticks collected from Australian wild animals from Victoria. The sequence data (in bp) (352 (gltA), 434 (ompA), 391 (17 kDa), and 267 (ompB)) for each locus were separately analysed using Neighbor Joining (NJ), Maximum Likelihood (ML), and Bayesian Inference (BI) methods. There was a concordance among the topologies of the BI, ML, and NJ trees (not shown); only ML trees are presented here. Nodal support is given as a posterior probability of BI and bootstrap values for NJ and ML. Sequences obtained in this study are shown in bold fonts. Trees were rooted using R. felis and R. bellii as outgroups. Each scale bar indicates the number of inferred substitutions per site.